Dynamic Sign Language Recognition Based on Convolutional Neural Networks and Texture Maps

  • Edwin J. Escobedo Cardenas Federal University of Ouro Preto
  • Lourdes Ramirez Cerna Federal University of Ouro Preto
  • Guillermo Camara-Chavez Federal University of Ouro Preto

Resumo


Sign language recognition (SLR) is a very challenging task due to the complexity of learning or developing descriptors to represent its primary parameters (location, movement, and hand configuration). In this paper, we propose a robust deep learning based method for sign language recognition. Our approach represents multimodal information (RGB-D) through texture maps to describe the hand location and movement. Moreover, we introduce an intuitive method to extract a representative frame that describes the hand shape. Next, we use this information as inputs to two three-stream and two-stream CNN models to learn robust features capable of recognizing a dynamic sign. We conduct our experiments on two sign language datasets, and the comparison with state-of-the-art SLR methods reveal the superiority of our approach to optimally combining texture maps and hand shape for SLR tasks.

Palavras-chave: CNN, sign language, texture maps

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Publicado
28/10/2019
CARDENAS, Edwin J. Escobedo ; CERNA, Lourdes Ramirez; CAMARA-CHAVEZ, Guillermo. Dynamic Sign Language Recognition Based on Convolutional Neural Networks and Texture Maps. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . DOI: https://doi.org/10.5753/sibgrapi.2019.9790.